sim_pop: Simulates evolution of DNA of a population
In philliplab/yasss: Yet Another Short Sequence Simulator

Description Usage Arguments Details Value Examples

Produce a set of DNA sequences by simulation a population with multiple generations allowing mutation and recombination

sim_pop(
  ancestors,
  r0 = 2,
  n_gen = NULL,
  n_pop = NULL,
  mutator = list(fun = "mutator_uniform_fun", args = list(mu = 0.01)),
  fitness_evaluator = list(fun = "fitness_evaluator_uniform_fun", args = NULL),
  ps_rate = 0,
  verbose = FALSE
)

`ancestors`	A list of DNA sequences with which to start the population. Include the same sequence multiple times to achieve a target ratio.
`r0`	The number of offspring each molecule produces. Currently restricted to being an integer, but this will become a more complex construct in future versions (GH issue #17).
`n_gen`	The number of generations to simulate.
`n_pop`	Stop the simulation when the population size exceeds this number.
`mutator`	A list with two elements fun and args specifying the name of the function that mutates parents into their offspring and the list of arguments said function requires.
`fitness_evaluator`	A list with two elements fun and args, specifying the name of the function that evaluates the fitness of each sequence and the list of arguments the function requires.
`ps_rate`	The chance that any given sequence will be a recombinant.
`verbose`	If TRUE, progress is printed to STDOUT.

Details: Simulated multiple generations TODO - expand this

A genealogy data structure. TODO: link to general documentation on a genealogy. GH issue #6.

# Five generations, ancestor 60 As, mutation rate 10% per base per generation
x <- sim_pop(ancestors = paste(rep("A", 60), collapse = ''),
             r0 = 2,
             n_gen = 5,
             n_pop = Inf,
             mutator = list(fun = "mutator_uniform_fun",
                            args = list(mu = 0.1)),
             fitness_evaluator = list(fun = "fitness_evaluator_uniform_fun",
                                      args = NULL))
# Prints sequences of current generation
x %>% filter(gen_num == max(gen_num)) %>% select('the_seq')

# Plots a histogram of the number of mutations between each parent
# and its offspring in the last generation
## Not run: 
ggplot(subset(x, gen_num == max(gen_num)), aes(n_mut)) + 
  geom_histogram(binwidth = 1)

## End(Not run)